BICAICcapushe {capushe}  R Documentation 
AICcapushe and BICcapushe
Description
These functions return the model selected by the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC).
Usage
AICcapushe(data, n)
BICcapushe(data, n)
Arguments
data 

n 

Details
The penalty shape value should be increasing with respect to the complexity value (column 3).
The complexity values have to be positive.
n
is necessary to compute AIC and BIC criteria. n
is the size of
sample used to compute the contrast values given in the data
matrix.
Do not confuse n
with the size of the model collection which is the number
of rows of the data
matrix.
Value
model 
The model selected by AIC or BIC. 
AIC 
The corresponding value of AIC (for AICcapushe only). 
BIC 
The corresponding value of BIC (for BICcapushe only). 
Author(s)
Vincent Brault
References
http://www.math.univtoulouse.fr/~maugis/CAPUSHE.html
http://www.math.upsud.fr/~brault/capushe.html
Article: Baudry, J.P., Maugis, C. and Michel, B. (2011) Slope heuristics: overview and implementation. Statistics and Computing, to appear. doi: 10.1007/ s1122201192361
See Also
capushe
for a model selection function including AIC, BIC,
the DDSE
algorithm and the Djump
algorithm.
Examples
data(datacapushe)
AICcapushe(datacapushe,n=1000)
BICcapushe(datacapushe,n=1000)